Collaboration group recommendations derived from request-action correlations

ABSTRACT

In response to a user-initiated interaction request sent by a user using an electronic communication, subsequent actions performed by other users that received the user-initiated interaction request are analyzed. A determination is made as to whether the subsequent actions performed by the other users that received the user-initiated interaction request correlate to an intended interaction result of the user-initiated interaction request. A visual representation of a collaboration model that correlates probabilities of successful collaborations between the user and the other users is generated in accordance with determined correlations between the subsequent actions performed by the other users and the intended interaction result. A collaboration recommendation based upon a degree of correlation between the subsequent actions performed by the other users and the intended interaction result represented within the collaboration model is provided in association with the visual representation of the collaboration model.

BACKGROUND

The present invention relates to collaboration group recommendations.More particularly, the present invention relates to collaboration grouprecommendations derived from request-action correlations.

Users of computing devices may utilize those devices to perform work,play games, send messages to other users, and for other purposes. Usersmay install and run different applications on the computing devices toperform these different types of functionality.

SUMMARY

A method includes analyzing, by a processor in response to auser-initiated interaction request sent by a user via an electroniccommunication, subsequent actions performed by other users that receivedthe user-initiated interaction request; determining whether thesubsequent actions performed by the other users that received theuser-initiated interaction request correlate to an intended interactionresult of the user-initiated interaction request; generating, inaccordance with determined correlations between the subsequent actionsperformed by the other users that received the user-initiatedinteraction request and the intended interaction result of theuser-initiated interaction request, a visual representation of acollaboration model that correlates probabilities of successfulcollaborations between the user and the other users; and providing, inassociation with the visual representation of the collaboration model, acollaboration recommendation based upon a degree of correlation betweenthe subsequent actions performed by the other users that received theuser-initiated interaction request and the intended interaction resultof the user-initiated interaction request represented within thecollaboration model.

A system that performs the method and a computer program product thatcauses a computer to perform the method are also described.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example of an implementation of a systemfor collaboration group recommendations derived from request-actioncorrelations according to an embodiment of the present subject matter;

FIG. 2 is a block diagram of an example of an implementation of a coreprocessing module capable of performing collaboration grouprecommendations derived from request-action correlations according to anembodiment of the present subject matter;

FIG. 3 is an illustration of an example of an implementation of avisual/graphical representation of a collaboration model according to anembodiment of the present subject matter;

FIG. 4 is a flow chart of an example of an implementation of a processfor collaboration group recommendations derived from request-actioncorrelations according to an embodiment of the present subject matter;and

FIG. 5 is a flow chart of an example of an implementation of a processfor collaboration group recommendations derived from request-actioncorrelations that includes both electronic communication analysis andcollaboration recommendations according to an embodiment of the presentsubject matter.

DETAILED DESCRIPTION

The examples set forth below represent the necessary information toenable those skilled in the art to practice the invention and illustratethe best mode of practicing the invention. Upon reading the followingdescription in light of the accompanying drawing figures, those skilledin the art will understand the concepts of the invention and willrecognize applications of these concepts not particularly addressedherein. It should be understood that these concepts and applicationsfall within the scope of the disclosure and the accompanying claims.

The subject matter described herein provides collaboration grouprecommendations derived from request-action correlations. The presenttechnology solves a recognized collaboration group identificationproblem by providing technology that includes a new form ofcomputational processing that evaluates subsequent actions of users inresponse to requests (e.g., inquiries for information, help, and otherforms of requests) and that identifies collaborative grouprecommendations in accordance with identified correlations between therequests and the subsequent actions of users to which the requests havebeen issued.

The technology described herein operates by analyzing, in response to auser-initiated interaction request sent by a user via an electroniccommunication, subsequent actions performed by other users that receivedthe user-initiated interaction request. A determination is maderegarding whether the subsequent actions performed by the other usersthat received the user-initiated interaction request correlate to anintended interaction result of the user-initiated interaction request.In accordance with determined correlations between the subsequentactions performed by other users that received the user-initiatedinteraction request and the intended interaction result of theuser-initiated interaction request, a visual representation of acollaboration model is generated that identifies probabilities ofsuccess regarding collaborations between the user and the other users.In association with the visual representation of the collaborationmodel, a collaboration recommendation is provided based upon a degree ofcorrelation between the subsequent actions performed by other users thatreceived the user-initiated interaction request and the intendedinteraction result of the user-initiated interaction request.

The technology described herein measures user/recipient responsivenessto requests of other users that are received by electroniccommunication, and measures a resulting effectiveness of different users(collaboration partners) in responding to the requests. Thisresponsiveness information and effectiveness information representdatasets related to the respective individual users and collaborationgroups. To extract information from these datasets, statistical modelsmay be employed to identify trends within a given dataset that anindividual user (e.g., a decision maker for a new collaboration attempt)may find useful for making a particular collaboration decision. Thesestatistical models may further be useful to a temporal degree,particularly as the datasets grow and the scope of pattern matchingwiden. Under these circumstances, the technology described hereinreduces the amount of processing to find real-time collaborationrecommendation data that represents one or more possible collaborationswith a high likelihood (e.g., degree, value, etc.) of being successful.Collaboration recommendations may be provided, using real-timerecommendation data, based upon communications/interactions that areusable in real time for collaboration decision making.

As such, the technology described herein operates by determining,assessing, and measuring affinities within communication streams, theoutcomes of these communication streams, whether the outcomes arepositive or negative, and to what degree the outcomes were positive ornegative. Metrics are accumulated from the communication streams andoutcomes that identify a degree of success (outcome) of a priorinteraction, and that identify how that degree of success influenced therequesting user and collaboration groups with which the user interacts.The requesting user may be shown in real time which other users andcollaboration groups are more likely to be of benefit for futurecollaborations.

A requesting user is provided with recommendations according to a set ofprocessing steps. These steps include gathering of communications(requests) from a user to other users and collaboration groups, andstatistically analyzing the gathered communications to determine theoutcome of the interactions between the user and the other collaborationpartners and determine the subject matter that the communicationinvolved.

A model derived from the gathered communications and statisticalanalysis may be used to determine which collaboration partners provide amore positive influence on the requesting user (e.g., help the most,etc.). The model may be further enhanced by aggregating which users andcollaboration partners provide the most benefit within a particularfield or subject matter area. The resulting models allow a real-timerecommendation engine to identify and illustrate a recommended preferredset of collaboration partners and associated subject areas forconsideration by a user that is interested in a new collaboration.

Several measures of influence may be utilized to identify one or morerecommended collaboration partners. These measures may be quantified asthe actions performed by a recipient following receipt of a communicatedrequest. For example, a measure of a change in vocabulary usage by areceiver of a communication after having read the original communicationmay measure the level of influence that the requesting user has on therecipient. Another measure may be whether the receiving user forwards areceived request to another user known by the recipient to be moreknowledgeable in a particular subject matter area. A further measure mayinclude whether the recipient responds to or performs any activity atall related to the request (which may be suggestive of a level ofinterest in the subject matter area, a level of knowledge on theparticular topic, or an amount of time available to assist with therequest, etc.). These and other types of actions by recipients afterreceiving a communicated request may be monitored to capturerecommendation data used by the technology described herein to providereal-time collaboration recommendations.

This recommendation data may be captured for a subset of users or forall users within a collaboration network/organization. Thisrecommendation data may be used to determine which users within acollaboration network are more likely to provide a positiveinfluence/outcome for a requesting user. Collaboration models may bebuilt/constructed from the analytics performed on the capturedrecommendation data.

Within the collaboration organization, groups of people with which tointeract/collaborate may be identified in real time based upon aparticular collaborative activity to produce a more positive outcomethan would otherwise be possible without the technology describedherein. Taking the real-time recommendation collaboration model further,groups of people may be identified as mentors and/or trainers for otherindividuals to improve business and/or personal productivity.

As described above, information related to user communications/requestsand subsequent actions of recipients may be gathered. This informationmay be stored within a database or other form of memory and analyzed todetermine the effectiveness of the subsequent actions of the recipientsat assisting the requesting user with the user's collaborationinterest/topic. These communication flows to/from the requesting usermay be processed using statistical analysis to determine which subjectmatter areas and/or which collaboration partner(s) exhibited the mostpositive influence regarding the outcome/result for the requesting user.Processing of these communication flows may include analyzing thetemporal real-time nature of these interactions, and predicting thesuccess of future collaborations.

To help assess the collaborative actions of the involved parties (e.g.,whether a particular collaboration partner contributes, declines torespond, or whether the parties are even associated with a subjectmatter area/topic), the messages and their associated subject matterarea (e.g., a topic such as “audio design”) may be monitored, and logsmay be created that identify users to which the message is sent. Thatinformation may be collected across a group of users and may beevaluated to help ascertain potential collaboration effectiveness andcompatibility among different users.

The following Table (1) illustrates a set of two predictive analyticsbased upon a set of communication flows including recommendation datathat is gathered/captured in response to an evaluation of thecommunication flows. The Table (1) represents one form of a visualrepresentation of a collaboration model that identifies probabilities ofsuccess regarding collaborations between a user and the other users.

TABLE (1) Example Communication Flow Recommendation Data Collab- Subjectoration Process Process Successful User Matter Partners ContributionImprovement Outcome Tom Audio Design Ben 50% Yes Yes Mary 30% Yes YesPat 20% Yes Yes Frank Encryption Paul 80% Yes No Mark 10% Yes No Peter10% No No

Table (1) shows two sets of derived recommendation data. The first setof recommendation data is related to a requesting user “Tom,” and isrelated to the subject matter area “audio design.” As can be seen fromthis first set of recommendation data, three collaboration partners arelisted, specifically “Ben,” “Mary,” and “Pat.” These collaborationpartners have respectively contributed in accordance with previousrequests and responses, and are thereby predicted based upon the derivedanalytics to contribute to future collaborations to a level of fiftypercent (50%) for Ben, thirty percent (30%) for Mary, and twenty percent(20%) for Pat. A process improvement resulting from the collaboration ispredicted for each of the three collaboration partners (e.g., “Yes”),and a successful outcome of the collaboration is predicted for each ofthe three collaboration partners (e.g., “Yes”).

The second set of recommendation data is related to a requesting user“Frank,” and is related to the subject matter area “encryption.” As canbe seen from this second set of recommendation data, three collaborationpartners are also listed, specifically “Paul,” “Mark,” and “Peter.”These collaboration partners respectively have contributed in accordancewith previous requests and responses, and are thereby predicted basedupon the derived analytics to contribute to future collaborations to alevel of eighty percent (80%) for Paul, and ten percent (10%) for eachof Mark and Peter. However, the process improvement results and outcomesuccess predictions are shown to be different within the second set ofrecommendation data. As can be seen, a process improvement resultingfrom the collaboration is predicted for Paul and Mark (e.g., “Yes”), buta process improvement is not predicted to result from the interactionwith Peter (e.g., “No”). Further, in all three interactions, the outcomeof the collaboration is predicted to be unsuccessful for eachcollaboration partner (e.g., “No”).

Using statistical inference, such as by use of a regression model andregression analysis, the information regarding the communication flowscaptured within Table (1) may be used to help plan for futurecollaborations. For example, using recommendation data gathered from ameeting between Tom and Ben/Mary/Pat at 10 am, a real-time determinationmay be made regarding how this interaction may influence Tom'scommunication with other people thereafter. Similarly, if Tom wants toadvance his skills in audio design for the purposes of, for example,career building, Tom may elect work primarily with Ben and to a lesserextent with Mary and Pat.

Further, based upon the example information captured in Table 1, Frankappears to have had moderate success collaborating with Paul within thesubject matter area of encryption, less success with Mark, and nosuccess with Peter. Frank may be directed/recommended to work more withPaul and less with Mark and Peter given the lack of process improvementand the lack of a successful outcome from those interactions.

A collaboration model may be formed, and a visual (e.g., graphical)representation of these relationships may be provided as graphical datathat may be used as part of a modeling workflow program or other userinterface. The visual representation of the collaboration model mayidentify probabilities of success regarding collaborations between theuser and the other users. An example visual representation of acollaboration model is illustrated and described further below inassociation with FIG. 3, which is deferred in favor of a description ofsystem components and componentry.

It should be noted that conception of the present subject matterresulted from recognition of certain limitations associated withonline-initiated technical collaborations (e.g., requests forinformation, help, etc.). For example, it was observed that users mayengage in collaborations for business or personal matters, and thatusers often collaborate with heterogeneous groups of people in attemptsto find the “right person” to assist with a particular issue or topic ofinterest. However, it was determined that the increasing complexity ofonline communications makes it difficult for users to understand howtheir requests for assistance on a particular topic are perceived byother users (e.g., whether a positive or negative response was invokedin the request recipients). It was further determined that where aparticular request was positively acted upon by one or more other users,those types of user interactions may be beneficial for futurecollaborations, with a corresponding de-emphasis where a particularrequest was not acted upon or was only marginally acted upon by one ormore other users. It was determined that new technology that analyzesresponse patterns of users and that provides users with information onthe various degrees of user responsiveness to their requests may assistusers in making better decisions when initiating future collaborationswith others and may encourage responsiveness, creativity, and problemsolving skills among the various collaborating users. It was furtherdetermined that this new technology would allow users of complexcomputing platforms to more effectively interact with other users andthereby increase their likelihoods of success in identifyingcontributors of merit for future collaborations. The present subjectmatter improves likelihoods of collaboration success by derivingcollaboration group recommendations by correlations over time of userrequests and corresponding actions by other users, as described aboveand in more detail below. As such, improved collaboration success may beobtained through use of the present technology.

The collaboration group recommendations derived from request-actioncorrelations described herein may be performed in real time to allowprompt identification of collaboration recommendations for particularpurposes using real-time recommendation data based uponcommunication/interactions to assist with decision making for usercollaborations. For purposes of the present description, real time shallinclude any time frame of sufficiently short duration as to providereasonable response time for information processing acceptable to a userof the subject matter described. Additionally, the term “real time”shall include what is commonly termed “near real time”—generally meaningany time frame of sufficiently short duration as to provide reasonableresponse time for on-demand information processing acceptable to a userof the subject matter described (e.g., within a portion of a second orwithin a few seconds). These terms, while difficult to precisely defineare well understood by those skilled in the art.

FIG. 1 is a block diagram of an example of an implementation of a system100 for collaboration group recommendations derived from request-actioncorrelations. A computing device_1 102 through a computing device_N 104communicate via a network 106 with several other devices. The otherdevices include a server_1 108 through a server_M 110. A database 112provides storage accessible by the respective devices within the system100, such as for data related to requests and subsequent actions, andanalytics to identify correlations and to derive collaboration grouprecommendations.

As will be described in more detail below in association with FIG. 2through FIG. 5, the computing device_1 102 through the computingdevice_N 104 may each provide automated collaboration grouprecommendations derived from request-action correlations. The automatedcollaboration group recommendations derived from request-actioncorrelations is based upon determining correlations among sets ofrequests issued from one user to one or more other users, and thesubsequent actions of the requested other user(s), to identifycompatible relationships for future collaborations. The presenttechnology may be implemented at a user computing device or serverdevice level, or by a combination of such devices as appropriate for agiven implementation. A variety of possibilities exist forimplementation of the present subject matter, and all such possibilitiesare considered within the scope of the present subject matter.

The network 106 may include any form of interconnection suitable for theintended purpose, including a private or public network such as anintranet or the Internet, respectively, direct inter-moduleinterconnection, dial-up, wireless, or any other interconnectionmechanism capable of interconnecting the respective devices.

The server_1 108 through the server_M 110 may include any device capableof providing data for consumption by a device, such as the computingdevice_1 102 through the computing device_N 104, via a network, such asthe network 106. As such, the server_1 108 through the server_M 110 mayeach include a web server, an application server, or other data serverdevice.

The database 112 may include a relational database, an object database,or any other storage type of device. As such, the database 112 may beimplemented as appropriate for a given implementation.

FIG. 2 is a block diagram of an example of an implementation of a coreprocessing module 200 capable of performing collaboration grouprecommendations derived from request-action correlations. The coreprocessing module 200 may be associated with either the computingdevice_1 102 through the computing device_N 104 or with the server_1 108through the server_M 110, as appropriate for a given implementation. Assuch, the core processing module 200 is described generally herein,though it is understood that many variations on implementation of thecomponents within the core processing module 200 are possible and allsuch variations are within the scope of the present subject matter.

Further, the core processing module 200 may provide different andcomplementary processing of request/action analytics for use in formingcollaboration group recommendations derived from request-actioncorrelations in association with each implementation. As such, for anyof the examples below, it is understood that any aspect of functionalitydescribed with respect to any one device that is described inconjunction with another device (e.g., sends/sending, etc.) is to beunderstood to concurrently describe the functionality of the otherrespective device (e.g., receives/receiving, etc.).

A central processing unit (CPU) 202 (“processor”) provides hardware thatperforms computer instruction execution, computation, and othercapabilities within the core processing module 200. A display 204provides visual information to a user of the core processing module 200and an input device 206 provides input capabilities for the user.

The display 204 may include any display device, such as a cathode raytube (CRT), liquid crystal display (LCD), light emitting diode (LED),electronic ink displays, projection, touchscreen, or other displayelement or panel. The input device 206 may include a computer keyboard,a keypad, a mouse, a pen, a joystick, touchscreen, voice commandprocessing unit, or any other type of input device by which the user mayinteract with and respond to information on the display 204.

A communication module 208 provides hardware, protocol stack processing,and interconnection capabilities that allow the core processing module200 to communicate with other modules within the system 100. Thecommunication module 208 may include any electrical, protocol, andprotocol conversion capabilities useable to provide interconnectioncapabilities, as appropriate for a given implementation. As such, thecommunication module 208 represents a communication device capable ofcarrying out communications with other devices.

A memory 210 includes a request/action storage area 212 that storesinformation usable to derive collaboration recommendations. The usableinformation may include initial user requests, subsequent actions ofrecipients of the requests, and other information in association withthe core processing module 200. The memory 210 also includes acollaboration processing area 214 that provides processing space foranalytics to derive collaboration recommendations.

It is understood that the memory 210 may include any combination ofvolatile and non-volatile memory suitable for the intended purpose,distributed or localized as appropriate, and may include other memorysegments not illustrated within the present example for ease ofillustration purposes. For example, the memory 210 may include a codestorage area, an operating system storage area, a code execution area,and a data area without departure from the scope of the present subjectmatter.

A request/action collaboration recommendation module 216 is alsoillustrated. The request/action collaboration recommendation module 216provides analytical processing of requests and subsequent actions, andderives future collaboration recommendations, for the core processingmodule 200, as described above and in more detail below. Therequest/action collaboration recommendation module 216 implements theautomated collaboration group recommendations derived fromrequest-action correlations of the core processing module 200.

It should also be noted that the request/action collaborationrecommendation module 216 may form a portion of other circuitrydescribed without departure from the scope of the present subjectmatter. The request/action collaboration recommendation module 216 mayform a portion of an interrupt service routine (ISR), a portion of anoperating system, or a portion of an application without departure fromthe scope of the present subject matter.

The database 112 is again shown within FIG. 2 associated with the coreprocessing module 200. As such, the database 112 may be operativelycoupled to the core processing module 200 without use of networkconnectivity, as appropriate for a given implementation.

The CPU 202, the display 204, the input device 206, the communicationmodule 208, the memory 210, the request/action collaborationrecommendation module 216, and the database 112 are interconnected viaan interconnection 218. The interconnection 218 may include a systembus, a network, or any other interconnection capable of providing therespective components with suitable interconnection for the respectivepurpose.

Though the different modules illustrated within FIG. 2 are illustratedas component-level modules for ease of illustration and descriptionpurposes, it should be noted that these modules may include anyhardware, programmed processor(s), and memory used to carry out thefunctions of the respective modules as described above and in moredetail below. For example, the modules may include additional controllercircuitry in the form of application specific integrated circuits(ASICs), processors, antennas, and/or discrete integrated circuits andcomponents for performing communication and electrical controlactivities associated with the respective modules. Additionally, themodules may include interrupt-level, stack-level, and application-levelmodules as appropriate. Furthermore, the modules may include any memorycomponents used for storage, execution, and data processing forperforming processing activities associated with the respective modules.The modules may also form a portion of other circuitry described or maybe combined without departure from the scope of the present subjectmatter.

Additionally, while the core processing module 200 is illustrated withand has certain components described, other modules and components maybe associated with the core processing module 200 without departure fromthe scope of the present subject matter. Additionally, it should benoted that, while the core processing module 200 is described as asingle device for ease of illustration purposes, the components withinthe core processing module 200 may be co-located or distributed andinterconnected via a network without departure from the scope of thepresent subject matter. Many possible arrangements for components of thecore processing module 200 are possible and all are considered withinthe scope of the present subject matter. It should also be understoodthat, though the database 112 is illustrated as a separate component forpurposes of example, the information stored within the database 112 mayalso/alternatively be stored within the memory 210 without departurefrom the scope of the present subject matter. Accordingly, the coreprocessing module 200 may take many forms and may be associated withmany platforms.

FIG. 3 is an illustration of an example of an implementation of avisual/graphical representation of a collaboration model 300. Thecollaboration model 300 represents a graphical representation ofrelationships that may be displayed as graphical data and used, forexample, as part of a career modeling workflow program. As describedabove, and in more detail below, user requests and subsequent actions ofrecipients of those requests may be monitored over time. Collaborationrecommendations may be derived in accordance with the responsiveness andsuccess of the subsequent actions of the recipients relative to theintended outcome of the requesting user. The collaborationrecommendations may be provided to the user in a graphical form of thecollaboration model 300, and the user may rapidly determine whichinteraction(s) with other users indicate a high probability for asuccessful conclusion related a particular type of collaborationobjective.

It should be noted that the graphical collaboration model 300illustrated in FIG. 3 is representative of a particular category ofcollaboration (e.g., audio design), though a different graphicalcollaboration model may be generated for each particular category ofcollaboration that is of interest to a particular user. It should alsobe noted that objects within the collaboration model 300 are not drawnto scale, though differences in sizes of the objects representdifferences in determined probabilities of success based upon analysisof requests and subsequent actions, as noted above with respect to thefirst data set in Table (1).

As can be seen in FIG. 3, the display 204 is illustrated as renderingthe visual/graphical representation of the collaboration model 300. Forpurposes of example, the collaboration model 300 is illustrated torepresent one particular category of recommendation for collaboration(e.g., “AUDIO DESIGN RECOMMENDATION: COLLABORATE WITH BEN—50%PROBABILITY OF SUCCESS”).

A graphic 302 represents predictions regarding an interaction with Benwithin the particular category of collaboration recommendation of audiodesign. The graphic 302 is illustrated as a largest graphic within thecollaboration model 300 and is shown to be rendered in a highestposition within the collaboration model 300, which represents that thefifty percent (50%) predicted likelihood of success based upon previousrequests and subsequent actions, is the largest value of the threevalues, as described above in association with Table (1).

Similarly, a graphic 304 represents predictions regarding an interactionwith Mary within the particular category of collaboration recommendationof audio design, again noted to be thirty percent (30%) in associationwith the description of Table (1). A graphic 306 represents predictionsregarding an interaction with Pat within the particular category ofcollaboration recommendation of audio design, again noted to be twentypercent (20%) in association with the description of Table (1).

The graphic 304 is illustrated with a size smaller (again not to scale)than the graphic 302 to illustrate the smaller probability of success ofcollaborations with Mary than with Ben. Further, the graphic 306 isillustrated with a size smaller (again not to scale) than the graphic304, and as such the graphic 302 also, to illustrate the smallerprobability of success of collaborations with Pat than with Mary, andwith Ben.

As such, the resulting recommendation may be seen from FIG. 3 torepresent that a “primary” interaction recommendation for collaborationfor audio design is with Ben. Similarly, a “secondary” interactionrecommendation for collaboration with Mary and Pat is provided, such asif time permits in view of the details above that collaboration is stillpredicted to be successful where Mary and Pat are included in acollaboration, and perhaps even more successful overall, again timepermitting.

The different shadings illustrated within FIG. 3 for the graphic 302,the graphic 304, and the graphic 306 also represent highlighting thatmay be used to draw the user's attention to the degree of compatibilityof the particular recommendations surfaced within the collaborationmodel for the particular category (again “audio design” for purposes ofexample). It should be noted that the highlighting may include colorrather than drawing line shapes, or otherwise as appropriate for a givenimplementation. For example, the graphic 302 may be rendered in thecolor green to represent a high degree of predicted compatibility forcollaborations. Similarly, the graphic 304 may be rendered in blue toshow the relative difference/decrease in degree of predictedcompatibility for collaborations. The graphic 306 may be rendered inyellow to show the further decreased relative degree of predictedcompatibility for collaborations. It should be noted that the exampleline shading patterns and colors described above are for purposes ofexample only, and should not be considered limiting in any manner. Thepresent technology may be implemented in view of the description hereinas appropriate for the given implementation.

FIG. 4 through FIG. 5 described below represent example processes thatmay be executed by devices, such as the core processing module 200, toperform the automated collaboration group recommendations derived fromrequest-action correlations associated with the present subject matter.Many other variations on the example processes are possible and all areconsidered within the scope of the present subject matter. The exampleprocesses may be performed by modules, such as the request/actioncollaboration recommendation module 216 and/or executed by the CPU 202,associated with such devices. It should be noted that time outprocedures and other error control procedures are not illustrated withinthe example processes described below for ease of illustration purposes.However, it is understood that all such procedures are considered to bewithin the scope of the present subject matter. Further, the describedprocesses may be combined, sequences of the processing described may bechanged, and additional processing may be added or removed withoutdeparture from the scope of the present subject matter.

FIG. 4 is a flow chart of an example of an implementation of a process400 for collaboration group recommendations derived from request-actioncorrelations. The process 400 represents a processor-implemented methodof performing the derived collaboration recommendations fromrequest-action patterns as described herein. At block 402, the process400 analyzes, by a processor in response to a user-initiated interactionrequest sent by a user via an electronic communication, subsequentactions performed by other users that received the user-initiatedinteraction request. At block 404, the process 400 determines whetherthe subsequent actions performed by the other users that received theuser-initiated interaction request correlate to an intended interactionresult of the user-initiated interaction request. At block 406, theprocess 400 generates, in accordance with determined correlationsbetween the subsequent actions performed by the other users thatreceived the user-initiated interaction request and the intendedinteraction result of the user-initiated interaction request, a visualrepresentation of a collaboration model that correlates probabilities ofsuccessful collaborations between the user and the other users. At block408, the process 400 provides, in association with the visualrepresentation of the collaboration model, a collaborationrecommendation based upon a degree of correlation between the subsequentactions performed by the other users that received the user-initiatedinteraction request and the intended interaction result of theuser-initiated interaction request represented within the collaborationmodel.

FIG. 5 is a flow chart of an example of an implementation of a process500 for collaboration group recommendations derived from request-actioncorrelations that includes both electronic communication analysis andcollaboration recommendations. The process 500 represents aprocessor-implemented method of performing the derived collaborationrecommendations from request-action patterns as described herein. Atdecision point 502, the process 500 begins higher-level iterativeprocessing by making a determination as to whether to perform recipientaction analytics. As described above, recipient actions responsive todetected receipt of user requests may be analyzed to determine recipientresponsiveness to requests of other users received by electroniccommunication and to determine responsiveness and effectivenessinformation usable to identify collaboration groups. Recipient actionanalytics may be used to determine recommendations for collaborationgroups responsive to collaboration requests. Affirmative processingresponsive to a determination to perform recipient action analytics willbe described in detail further below. As such, in response todetermining not to perform recipient action analytics (e.g., where theanalytics have already been performed), the process 500 makes adetermination at decision point 504 as part of the higher-leveliterative processing as to whether a collaboration request from a userhas been detected. A collaboration request from a user may include acollaboration topic or category for purposes of the present description.A collaboration recommendation may be generated responsive to a usercollaboration request based upon information derived from performing therecipient action analytics, and affirmative processing responsive to acollaboration request will also be described further below. As such, inresponse to determining at decision point 504 that a collaborationrequest has not been detected, the process 500 returns to decision point502 and iterates as described above.

Returning to the description of decision point 502, in response todetermining to perform recipient action analytics, the process 500begins monitoring communication data from disparate data sources atblock 506. The disparate data sources may include, among others, webconferences, instant messaging (IM), electronic mail (email) messages,and other forms of data sources. The monitoring may include textanalysis of written communications, voice analysis of web conferences,and other forms of monitoring as appropriate for a given implementation.

At block 508, the process 500 identifies user requests within thedisparate data sources. The user requests may be considereduser-initiated interaction requests. Identification of user requests mayalso include a determination of one or more intended interaction resultsof a user-initiated interaction request. It should be understood thatthe processing to monitor data sources and identify user requests isconsidered to be iterative, and the subsequent description operatesresponsive to each such user request.

At block 510, the process 500 monitors subsequent recipient actions,using text and/or voice analysis of electronic communications. Forexample, responses may be monitored to capture information with respectto whether the recipients responded to the request, dismissed therequest, or performed some other action.

At block 512, the process 500 determines, from text (and/or voice)analysis of electronic communications initiated by the other users afterreceipt of the user-initiated interaction request, whether the otherusers performed actions consistent with the intended interaction resultof user-initiated interaction request and a degree of assistanceprovided to the requesting user. The actions may include providing aninformative response or other actions as appropriate for a givenimplementation.

At block 514, the process 500 determines, from text (and/or voice)analysis of subsequent electronic communications initiated by the userafter receipt of an electronic communication response from at least oneof the other users, whether the response positively influenced the userthat sent the user-initiated interaction request. For example, apositive influence on a requesting user may include the user following ahypertext link (e.g., a uniform resource locator (URL)) provided by acolleague responsive to the user's request for assistance, or anotherform of action that is detectable in response to the response.

At block 516, the process 500 identifies a request/action category fromthe requests and subsequent actions of the recipients. Therequest/action category may include, for each particular request/actionset, one or more of topics, subject matter areas, or other forms ofcategories usable to identify collaboration requests.

At block 518, the process 500 identifies and stores, such as within thememory 210 or the database 112, collaboration metrics between theintended interaction results and the outcomes/results of the requests.For example, the process 500 may identify collaboration metrics thatspecify a likelihood of success of future collaborations between theuser and at least one of the other users that received theuser-initiated interaction request.

At block 520, the process 500 determines and stores probabilities ofsuccess for future collaborations in the identified request/actioncategory according to the statistical analysis of the requests andresponsive actions of other users. For example, the process 500 mayperform statistical analysis on the outcomes/results of the user request(e.g., the requests and responsive actions of other users), and anyactions performed by the requesting user in response to receiving aresponse to the request. The process 500 returns to decision point 502and iterates as described above.

Returning to the description of decision point 504, as described above,a collaboration request from a user may include a collaboration topic orcategory for purposes of the present description. In response todetermining at decision point 504 that a collaboration request from auser has been detected, the process 500 creates a collaboration model atblock 524. For example, the collaboration model may be constructedaccording to the topic associated with the collaboration request bycorrelating the collaboration metrics derived from previous userrequests (of the same or a different user) and responsive actionsperformed by recipients of the user requests. The collaboration metricsidentify probabilities of successful collaborations between the user andthe other users. The collaboration model may include users,collaboration partners, process contributions, determinations of processimprovement, and the probabilities of success assigned to the respectivecollaboration partners. The collaboration model may further includemultiple categories where multiple categories or topics are received inassociation with the collaboration request.

At block 526, the process 500 generates a visual representation of thecollaboration model. The process 500 may generate the visualrepresentation of the collaboration model that identifies probabilitiesof successful collaborations between the user and the other users inaccordance with determined correlations between the subsequent actionsperformed by other users that received the (previous) user-initiatedinteraction request and the intended interaction result of theuser-initiated interaction request. The visual representation mayfurther include a collaboration recommendation that specifies aprobability of success for a future collaboration between usersidentified in the collaboration recommendation. The visualrepresentation of the collaboration model may be generated as one of agraphical representation of the collaboration model, a table, a listing,or other visual representation that respectively identifies informationwithin the collaboration model.

At block 528, the process 500 outputs the visual representation of thecollaboration model and at least one collaboration recommendation.Outputting of the visual representation of the collaboration model andat least one collaboration recommendation may include rendering thevisual representation of the collaboration model and recommendation(s)on a display, such as the display 204, may including sending the visualrepresentation/recommendation(s) to another device for rendering, orotherwise as appropriate for a given implementation. The process 500returns to decision point 502 and iterates as described above.

As such, the process 500 performs analytics on electronic communicationsfrom disparate data sources over time to identify requests from usersand subsequent actions performed by recipients of the requests.Collaboration metrics are derived in accordance with statisticalanalysis of the requests/actions. Collaboration requests are processedby generating a collaboration model including one or moretopics/categories of requested collaborations, displaying a visual(e.g., graphical) representation of the collaboration model, anddisplaying at least one collaboration recommendation to the requestinguser.

As described above in association with FIG. 1 through FIG. 5, theexample systems and processes provide collaboration grouprecommendations derived from request-action correlations. Many othervariations and additional activities associated with collaboration grouprecommendations derived from request-action correlations are possibleand all are considered within the scope of the present subject matter.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art basedupon the teachings herein without departing from the scope and spirit ofthe invention. The subject matter was described to explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A method, comprising: analyzing, by a processorin response to a user-initiated interaction request sent by a user viaan electronic communication, subsequent actions performed by other usersthat received the user-initiated interaction request; determiningwhether the subsequent actions performed by the other users thatreceived the user-initiated interaction request correlate to an intendedinteraction result of the user-initiated interaction request;generating, in accordance with determined correlations between thesubsequent actions performed by the other users that received theuser-initiated interaction request and the intended interaction resultof the user-initiated interaction request, a visual representation of acollaboration model that correlates probabilities of successfulcollaborations between the user and the other users; and providing, inassociation with the visual representation of the collaboration model, acollaboration recommendation based upon a degree of correlation betweenthe subsequent actions performed by the other users that received theuser-initiated interaction request and the intended interaction resultof the user-initiated interaction request represented within thecollaboration model.
 2. The method of claim 1, where determining whetherthe subsequent actions performed by the other users that received theuser-initiated interaction request correlate to the intended interactionresult of the user-initiated interaction request comprises determining,from text analysis of electronic communications initiated by the otherusers after receipt of the user-initiated interaction request, whetherthe other users performed actions consistent with the intendedinteraction result of the user-initiated interaction request.
 3. Themethod of claim 1, where determining whether the subsequent actionsperformed by the other users that received the user-initiatedinteraction request correlate to the intended interaction result of theuser-initiated interaction request comprises determining, from textanalysis of a subsequent electronic communication initiated by the userafter receipt of an electronic communication response from at least oneof the other users, whether the electronic communication responsepositively influenced the user that sent the user-initiated interactionrequest.
 4. The method of claim 1, where the collaborationrecommendation specifies a probability of success for a futurecollaboration between users identified in the collaborationrecommendation.
 5. The method of claim 1, further comprising identifyingcollaboration metrics that specify probabilities of success of futurecollaborations between the user and at least one of the other users thatreceived the user-initiated interaction request.
 6. The method of claim1, further comprising creating the collaboration model by correlatingidentified collaboration metrics that specify probabilities of successof future collaborations between the user and at least one of the otherusers that received the user-initiated interaction request.
 7. Themethod of claim 1, where generating, in accordance with the determinedcorrelations between the subsequent actions performed by the other usersthat received the user-initiated interaction request and the intendedinteraction result of the user-initiated interaction request, the visualrepresentation of the collaboration model that identifies theprobabilities of successful collaborations between the user and theother users comprises: generating one of a graphical representation ofthe collaboration model and a table, where the generated one of thegraphical representation of the collaboration model and the tableidentifies information within the collaboration model.
 8. A system,comprising: a display device; and a processor programmed to: analyze, inresponse to a user-initiated interaction request sent by a user via anelectronic communication, subsequent actions performed by other usersthat received the user-initiated interaction request; determine whetherthe subsequent actions performed by the other users that received theuser-initiated interaction request correlate to an intended interactionresult of the user-initiated interaction request; generate on thedisplay, in accordance with determined correlations between thesubsequent actions performed by the other users that received theuser-initiated interaction request and the intended interaction resultof the user-initiated interaction request, a visual representation of acollaboration model that correlates probabilities of successfulcollaborations between the user and the other users; and provide, inassociation with the visual representation of the collaboration model, acollaboration recommendation based upon a degree of correlation betweenthe subsequent actions performed by the other users that received theuser-initiated interaction request and the intended interaction resultof the user-initiated interaction request represented within thecollaboration model.
 9. The system of claim 8, where, in beingprogrammed to determine whether the subsequent actions performed by theother users that received the user-initiated interaction requestcorrelate to the intended interaction result of the user-initiatedinteraction request, the processor is programmed to determine, from textanalysis of electronic communications initiated by the other users afterreceipt of the user-initiated interaction request, whether the otherusers performed actions consistent with the intended interaction resultof the user-initiated interaction request.
 10. The system of claim 8,where, in being programmed to determine whether the subsequent actionsperformed by the other users that received the user-initiatedinteraction request correlate to the intended interaction result of theuser-initiated interaction request, the processor is programmed todetermine, from text analysis of a subsequent electronic communicationinitiated by the user after receipt of an electronic communicationresponse from at least one of the other users, whether the electroniccommunication response positively influenced the user that sent theuser-initiated interaction request.
 11. The system of claim 8, where thecollaboration recommendation specifies a probability of success for afuture collaboration between users identified in the collaborationrecommendation.
 12. The system of claim 8, where the processor isfurther programmed to: identify collaboration metrics that specifyprobabilities of success of future collaborations between the user andat least one of the other users that received the user-initiatedinteraction request; and create the collaboration model by correlatingthe identified collaboration metrics that specify the probabilities ofsuccess of future collaborations between the user and at least one ofthe other users that received the user-initiated interaction request.13. The system of claim 8, where, in being programmed to generate on thedisplay, in accordance with the determined correlations between thesubsequent actions performed by the other users that received theuser-initiated interaction request and the intended interaction resultof the user-initiated interaction request, the visual representation ofthe collaboration model that identifies the probabilities of successfulcollaborations between the user and the other users, the processor isprogrammed to: generate on the display one of a graphical representationof the collaboration model and a table, where the generated one of thegraphical representation of the collaboration model and the tableidentifies information within the collaboration model.
 14. A computerprogram product, comprising: a computer readable storage medium havingcomputer readable program code embodied therewith, where the computerreadable storage medium is not a transitory signal per se and where thecomputer readable program code when executed on a computer causes thecomputer to: analyze, in response to a user-initiated interactionrequest sent by a user via an electronic communication, subsequentactions performed by other users that received the user-initiatedinteraction request; determine whether the subsequent actions performedby the other users that received the user-initiated interaction requestcorrelate to an intended interaction result of the user-initiatedinteraction request; generate, in accordance with determinedcorrelations between the subsequent actions performed by the other usersthat received the user-initiated interaction request and the intendedinteraction result of the user-initiated interaction request, a visualrepresentation of a collaboration model that correlates probabilities ofsuccessful collaborations between the user and the other users; andprovide, in association with the visual representation of thecollaboration model, a collaboration recommendation based upon a degreeof correlation between the subsequent actions performed by the otherusers that received the user-initiated interaction request and theintended interaction result of the user-initiated interaction requestrepresented within the collaboration model.
 15. The computer programproduct of claim 14, where, in causing the computer to determine whetherthe subsequent actions performed by the other users that received theuser-initiated interaction request correlate to the intended interactionresult of the user-initiated interaction request, the computer readableprogram code when executed on the computer causes the computer todetermine, from text analysis of electronic communications initiated bythe other users after receipt of the user-initiated interaction request,whether the other users performed actions consistent with the intendedinteraction result of the user-initiated interaction request.
 16. Thecomputer program product of claim 14, where, in causing the computer todetermine whether the subsequent actions performed by the other usersthat received the user-initiated interaction request correlate to theintended interaction result of the user-initiated interaction request,the computer readable program code when executed on the computer causesthe computer to determine, from text analysis of a subsequent electroniccommunication initiated by the user after receipt of an electroniccommunication response from at least one of the other users, whether theelectronic communication response positively influenced the user thatsent the user-initiated interaction request.
 17. The computer programproduct of claim 14, where the collaboration recommendation specifies aprobability of success for a future collaboration between usersidentified in the collaboration recommendation.
 18. The computer programproduct of claim 14, where the computer readable program code whenexecuted on the computer further causes the computer to identifycollaboration metrics that specify probabilities of success of futurecollaborations between the user and at least one of the other users thatreceived the user-initiated interaction request.
 19. The computerprogram product of claim 14, where the computer readable program codewhen executed on the computer further causes the computer to create thecollaboration model by correlating identified collaboration metrics thatspecify probabilities of success of future collaborations between theuser and at least one of the other users that received theuser-initiated interaction request.
 20. The computer program product ofclaim 14, where, in causing the computer to generate, in accordance withthe determined correlations between the subsequent actions performed bythe other users that received the user-initiated interaction request andthe intended interaction result of the user-initiated interactionrequest, the visual representation of the collaboration model thatidentifies the probabilities of successful collaborations between theuser and the other users, the computer readable program code whenexecuted on the computer causes the computer to: generate one of agraphical representation of the collaboration model and a table, wherethe generated one of the graphical representation of the collaborationmodel and the table identifies information within the collaborationmodel.